We implemented the KCNet with PyTorch [1]} to boost the training speed supported by GPUs; PyTorch supports the above three datasets so that they can be easily used. Therefore, we conducted our experiments after only applying a standard normalization to them without the use of any data-augmentation method. We also implement the ELM model and a Fully-trained Single-Hidden-layer Neural network (FSHN) and test them to compare the performance with our model. The ELM was introduced as a baseline classifier by [2]}, and its characteristics are very similar to our model. We formulated the ELM model in a method that is often used in practice [3]}, [4]}, [5]} as follows: i) its hidden weights \(\textbf {w}_{i} = [\textbf {a}_{i}^{T}, b_{i}]^{T}\)  were defined by drawing uniform random variables \(\textbf {a}_{i} \sim \mathbb {U}(-1,1)\)  and \(b_{i} \sim \mathbb {U}(0,1)\) ; ii) the sigmoid function is used as a nonlinearity function \(g\) ; and iii) the output weight \(\beta \)  was estimated using the Moore–Penrose inverse. FSHN was implemented using the linear models followed by the ReLU function and optimized using the stochastic gradient descent, supported by PyTorch. The number of hidden nodes in the FSHN was set to have the number of adaptable parameters similar to KCNet. For hyperparameter setting in the DOA-applied KCNet and the Ensemble-DOA-applied KCNet, the original training set was randomly split into a training set and a validation set with a 5:1 ratio every epoch. See Appendix REF  for details of methods, hyperparameter search spaces, and implementations.
